Reinforcement Learning for Adaptive Video Compressive Sensing
This work addresses a research gap in video SCI systems for real-time applications, though it is incremental as it builds on existing methods.
The paper tackles the problem of adapting the compression ratio in video snapshot compressive imaging (SCI) for different scenes by applying reinforcement learning, achieving adaptive sensing that can be implemented in low cost and real time.
We apply reinforcement learning to video compressive sensing to adapt the compression ratio. Specifically, video snapshot compressive imaging (SCI), which captures high-speed video using a low-speed camera is considered in this work, in which multiple (B) video frames can be reconstructed from a snapshot measurement. One research gap in previous studies is how to adapt B in the video SCI system for different scenes. In this paper, we fill this gap utilizing reinforcement learning (RL). An RL model, as well as various convolutional neural networks for reconstruction, are learned to achieve adaptive sensing of video SCI systems. Furthermore, the performance of an object detection network using directly the video SCI measurements without reconstruction is also used to perform RL-based adaptive video compressive sensing. Our proposed adaptive SCI method can thus be implemented in low cost and real time. Our work takes the technology one step further towards real applications of video SCI.